Study of HFQ forming process on lightweight alloy components

In order to reduce CO2 emissions and improve fuel efficiency for the aerospace industry, a leading edge sheet metal forming technology, namely solution heat treatment, forming and in-die quenching (HFQ) was utilised to form lightweight, complex-shaped components, efficiently and cost-effectively. The work performed in this research project contains two major achievements. The first achievement is successfully forming a complex AA2060 (Al-Li alloy) wing stiffener demonstrator part, and an L-shape AA7075 demonstrator part, without necking or fracture, using HFQ forming technology. The feasibility of forming the aluminium alloys was based on a series of fundamental experimental tests including uniaxial tensile test, isothermal forming limit test and artificial aging test. The second achievement is the development of a novel forming limit prediction model, namely the viscoplastic-Hosford-MK model. This model enables the forming limit prediction of AA2060 and AA7075 alloys under hot stamping conditions, featuring non-isothermal and complex loading conditions. This prediction model fills a significant need in industry for accurately predicting the forming limit of aluminium alloys under such complex forming conditions. The effectiveness of the developed model was analytically verified for AA2060, demonstrating accurate material responses to cold die quenching, strain rate and loading path changes. By applying the developed model to the hot stamping of an AA2060 component, its accuracy was successfully validated. Furthermore, the viscoplastic-Hosford-MK model was also demonstrated for use in industry by determining the optimum initial blank shape of an L-shape AA7075 component. An iterative simulation procedure implementing the forming limit prediction model was used to arrive at an optimum blank shape by the minimisation of the failure criterion. The optimised initial blank shape design was applied in the experimental hot stamping of a demonstrator AA7075 component. The accuracy of the developed model was validated by the successful forming of the component, without necking or fracture.Open Acces

Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention

Deep neural networks, including recurrent networks, have been successfully applied to human activity recognition. Unfortunately, the final representation learned by recurrent networks might encode some noise (irrelevant signal components, unimportant sensor modalities, etc.). Besides, it is difficult to interpret the recurrent networks to gain insight into the models' behavior. To address these issues, we propose two attention models for human activity recognition: temporal attention and sensor attention. These two mechanisms adaptively focus on important signals and sensor modalities. To further improve the understandability and mean F1 score, we add continuity constraints, considering that continuous sensor signals are more robust than discrete ones. We evaluate the approaches on three datasets and obtain state-of-the-art results. Furthermore, qualitative analysis shows that the attention learned by the models agree well with human intuition.Comment: 8 pages. published in The International Symposium on Wearable Computers (ISWC) 201

Hot stamping of an Al-Li alloy: a feasibility study

The feasibility of forming a third generation aluminium-lithium alloy (AA2060) into a complex shaped panel component, was studied by using an advanced forming technology called solution heat treatment, cold die forming and in-die quenching (HFQa) process. The main challenges using HFQ technology to form complex shaped AA2060 component was to find out optimum forming parameters, such as forming temperature, forming speed, lubrication condition and blank holding force. In this paper, the optimum forming temperature was mainly concerned. The flow stresses of AA2060 were obtained at different temperatures ranging from 350 to 520 °C at the strain rate of 2 s−1. The suitable temperature to achieve the adequate ductility was found at 470 °C. By forming the AA2060 blanks at the optimum forming temperature, experimental results exhibited the feasibility for forming complex-shaped AA2060 components. The formed components were analysed through strain measurements. The post-form mechanical properties of AA2060 were assessed using hardness and tensile tests

A population-based study on prevalence and risk factors of gastroesophageal reflux disease in the Tibet Autonomous Region, China

Objective To investigate the prevalence and risk factors of gastroesophageal reflux disease (GERD) in the Tibet Autonomous Region, China. Methods In this cross-sectional study, a stratified random sampling method was used for collecting samples in the Tibet Autonomous Region. A total of 10,000 individuals were selected from October 2016 to June 2017. A previously-published, validated questionnaire including six items related to the symptoms of GERD was used for evaluating GERD. In addition, basic demographic data, lifestyle, dietary habits, medical history and family history of GERD were investigated to identify risk factors of GERD. Results A total of 5,680 completed questionnaires were collected and analyzed. The prevalence of GERD in this area was 10.8%. Age (30–40 years vs. under 18 years, odds ratio (OR): 3.025; 40–50 years vs. under 18 years, OR: 4.484), education level (high school vs. primary, OR: 0.698; university vs. primary, OR: 2.804), ethnic group (Han vs. Tibetan, OR: 0.230; others vs. Tibetan, OR: 0.304), altitude of residence (4.0–4.5 km vs. 2.5–3.0 km, OR: 2.469), length of residence (<5 years vs. ≥5 years, OR: 2.218), Tibetan sweet tea (yes vs. no, OR: 2.158), Tibetan barley wine (yes vs. no, OR: 1.271), Tibetan dried meat (yes vs. no, OR: 1.278) and staying up late (yes vs. no, OR: 1.223) were significantly (all P < 0.05) and independently associated with GERD. Conclusions The prevalence of GERD is high in the Tibet Autonomous Region, China. Geographic conditions, ethnic group and lifestyle are risk factors for GERD

Electrophoretic deposition of nanostructured-TiO2/chitosan composite coatings on stainless steel

Novel chitosan composite coatings containing titania nanoparticles (n-TiO2) for biomedical applications were developed by electrophoretic deposition (EPD) from ethanol–water suspensions. The optimal ethanol–water ratio was studied in order to avoid bubble formation during the EPD process and to ensure homogeneous coatings. Different n-TiO2 contents (0.5–10 g L−1) were studied for a fixed chitosan concentration (0.5 g L−1) and the properties of the electrophoretic coatings obtained were characterized. Coating composition was analyzed by thermogravimetric analysis (TG), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis. Scanning electron microscopy (SEM) was employed to study both the surface and the cross section morphology of the coatings, and the thicknesses (2–6 μm) of the obtained coatings were correlated with the initial ceramic content. Contact angle measurements, as a preliminary study to predict hypothetic protein attachment on the coatings, were performed for different samples and the influence of a second chitosan layer on top of the coatings was also tested. Finally, the electrochemical behavior of the coatings, evaluated by polarization curves in DMEM at 37 °C, was studied in order to assess the corrosion resistance provided by the n-TiO2/chitosan coatings

Dynamic User Optimal Signal Design at Isolated Intersections

This paper presents a proposal of novel signal design problem at isolated intersections, which assumes that the effective green times assigned to each signal phase follow dynamic user optimal (DUO) principle. At the DUO state, the average delays of vehicles using the signal phases with positive additional green times (the assigned effective green times minus the minimum effective green times) are equal and maximum. The proposed signal design problem is formulated as a variational inequality (VI) problem. The point queue (PQ) model is applied to represent traffic dynamics and to generate the cumulative traffic flows, which is further used to estimate the average delay of each signal phase. The existence of the solution of the proposed VI problem is proved and a solution algorithm based on the method of successive averages (MSA) is developed to solve the proposed signal design problem. Finally, a sample intersection is used to illustrate the application of the proposed model and the solution algorithm

Mechanical Load-Induced Atomic-Scale Deformation Evolution and Mechanism of SiC Polytypes Using Molecular Dynamics Simulation

Silicon carbide (SiC) is a promising semiconductor material for making high-performance power electronics with higher withstand voltage and lower loss. The development of cost-effective machining technology for fabricating SiC wafers requires a complete understanding of the deformation and removal mechanism. In this study, molecular dynamics (MD) simulations were carried out to investigate the origins of the differences in elastic&ndash;plastic deformation characteristics of the SiC polytypes, including 3C-SiC, 4H-SiC and 6H-SiC, during nanoindentation. The atomic structures, pair correlation function and dislocation distribution during nanoindentation were extracted and analyzed. The main factors that cause elastic&ndash;plastic deformation have been revealed. The simulation results show that the deformation mechanisms of SiC polytypes are all dominated by amorphous phase transformation and dislocation behaviors. Most of the amorphous atoms recovered after completed unload. Dislocation analysis shows that the dislocations of 3C-SiC are mainly perfect dislocations during loading, while the perfect dislocations in 4H-SiC and 6H-SiC are relatively few. In addition, 4H-SiC also formed two types of stacking faults

Boosted Dynamic Neural Networks

Early-exiting dynamic neural networks (EDNN), as one type of dynamic neural networks, has been widely studied recently. A typical EDNN has multiple prediction heads at different layers of the network backbone. During inference, the model will exit at either the last prediction head or an intermediate prediction head where the prediction confidence is higher than a predefined threshold. To optimize the model, these prediction heads together with the network backbone are trained on every batch of training data. This brings a train-test mismatch problem that all the prediction heads are optimized on all types of data in training phase while the deeper heads will only see difficult inputs in testing phase. Treating training and testing inputs differently at the two phases will cause the mismatch between training and testing data distributions. To mitigate this problem, we formulate an EDNN as an additive model inspired by gradient boosting, and propose multiple training techniques to optimize the model effectively. We name our method BoostNet. Our experiments show it achieves the state-of-the-art performance on CIFAR100 and ImageNet datasets in both anytime and budgeted-batch prediction modes. Our code is released at https://github.com/SHI-Labs/Boosted-Dynamic-Networks